Engineering Design, Industrial Design
Terawatt Current solar thermal systems are only achieving 60% of HOT WATER and do not contribute to home heating at all. The Terawatt Solar Thermal Tracker delivers 85% hot water & 60% of home heating needs and 100% domestic air-conditioning needs. Currently solar thermal manufacturers are restricted to installing a certain size of systems due to high sun rays in summer months hitting the tubes and causing high temperatures and over heating the system. Our system is stand alone that can track the sun which creates 46% more energy than fixed systems and our patent pending sensor prevents overheating by moving the array away from the sun at high temperatures. The average household oil bill is £1050 per year and with grants available and savings accounted for customers can earn £13615 over 7 years. With solar thermal not recognised we need to educate customers on the savings, the product and also look at options of product development in particular the aesthetics of the system. Intesyns i-Magine predictive heating controller from InteSys Ltd creates a building energy model on a microchip through machine learning, using readings from its sensors. After an initial learning stage, the model enables the controller to do a short-term prediction of temperatures in a building and consequently it delivers the right amount of heat at all times. This achieves energy saving of at least 20%. A number of competitive technologies, most notably Nest Thermostat, also claim learning capabilities, however these are limited to user set temperatures and times, and therefore merely replicating functionality of conventional devices. How should this controller be positioned on the market in order to be more desirable than the competition? Does it matter what it looks like, considering that it is currently in a kind of ‘boiler suit’ in comparison with ‘smart casual’ competition, such as Nest thermostat (see Figure 1 below)? Is predictive function enough to make it more competitive? What other functions would make it more desirable than the completion? What other things can prediction do for us in other fields? Onlicar We collect vehicle engine data and GPS to monitor how well a vehicle has been driven, maintained and repaired. Our initial customer is a fleet manager who is responsible for all of the company’s vehicles. He will need to be able to seamlessly visualize the location of all the vehicles, as well as obtain an overview of the fleet performance using certain metrics, for example average speed, fuel efficiency, engine health, etc. We have already built a prototype and an off-the-shelf front-end theme. We would like to collaborate to explore the user experience and user interface further. Please see below for screen shot of the live system as it is now. Commutable Commutable are developing a new software model for public transport on buses, which will enable a bus fleet to be run using both dynamic scheduling and dynamic routing.The goal of this project is to improve efficiency and utilisation of a municipal bus operator’s existing fleet by removing the restrictions of fixed routes and schedules, and running the service based on continually-recalculated predicted demand. We are seeking collaborators to help develop a mathematical simulation of a real-world bus service, to allow us to test the impact of changes to the bus’ routes or timetables. Skills in programming, modelling, algorithms, artificial intelligence and statistics are particularly relevant. Information about each day’s performance is fed back into the system to allow it to improve over time. The simulation will allow us to test various optimisations, and identify knock-on effects and inefficiencies that may result from these changes.